g5_intro.py

#import libraries
from datetime import datetime, timedelta,date
import pandas as pd
%matplotlib inline
from sklearn.metrics import classification_report,confusion_matrix
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from __future__ import division
from sklearn.cluster import KMeans

#do not show warnings
import warnings
warnings.filterwarnings("ignore")

#import plotly for visualization
import plotly.plotly as py
import plotly.offline as pyoff
import plotly.graph_objs as go

#import machine learning related libraries
from sklearn.svm import SVC
from sklearn.multioutput import MultiOutputClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
import xgboost as xgb
from sklearn.model_selection import KFold, cross_val_score, train_test_split

#initiate plotly
pyoff.init_notebook_mode()

#import the csv
tx_data = pd.read_csv('data.csv')

#print first 10 rows
tx_data.head(10)

#convert date field from string to datetime
tx_data['InvoiceDate'] = pd.to_datetime(tx_data['InvoiceDate'])

#create dataframe with uk data only
tx_uk = tx_data.query("Country=='United Kingdom'").reset_index(drop=True)